Recent works with deep learning methods such as convolutional neural networks (CNNs) or CNN with recurrent neural networks (CNN-RNNs), use spectrogram or raw waveform as input and have shown impressive results [1]}, [2]}, [3]}. These data-driven methods automatically learn the emotion-related representation, however, the role of individual speech attributes in the decision making process is not clear due to the lack of explainability. On the other hand, the generalization of these methods remains an open problem, especially when the audio-data for train and test are substantially different in terms of language and speech quality [4]}.
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